Senior ML Engineer

Welcome to Your Next Adventure! What You'll Get Your Hands On Develop and deploy machine learning and optimization algorithms powering Snoonu's hybrid autonomous logistics platform, enabling intelligent coordination across human couriers, ground robots, and drones.

Design and implement advanced optimization strategies for task allocation, routing, scheduling, and dispatch decision-making across a multi-agent fleet environment.

Develop and train multi-agent reinforcement learning (MARL) models to improve fleet-wide coordination efficiency under dynamic demand and traffic conditions. Build demand forecasting and spatiotemporal prediction models using modern architectures (e. g.

, Transformers, temporal models, graph-based methods) to support proactive positioning and better resource planning.

Develop energy-aware and environment-aware optimization models to improve battery utilization, charging schedules, delivery sequencing, and overall operational efficiency under Qatar's climate constraints.

Integrate ML optimization models into the Robotics-as-a-Service (RaaS) orchestration platform in collaboration with platform/backend engineers, ensuring reliability and low-latency decision making.

Evaluate model performance using defined metrics such as routing efficiency, ETA prediction accuracy, fleet coordination latency, and energy impact—supporting TRL advancement and pilot readiness.

Implement experimentation pipelines including offline benchmarking, simulation validation, and controlled field pilot evaluation to improve model accuracy and generalization.

Support continuous learning and model lifecycle management, including monitoring, retraining strategies, and mitigation of model drift using operational datasets.

Document model architecture, assumptions, validation results, and experimentation methods to support R&D reporting, internal knowledge sharing, and stakeholder alignment.

The Magic You Bring Bachelor's or Master's degree in Machine Learning, Artificial Intelligence, Computer Science, Data Science, Operations Research, or a related field. (PhD is a plus for research-driven optimisation work.) 2–4 years of backend development experience, with strong hands-on Python/C++ expertise.

Strong analytical and problem-solving skills with the ability to work on complex real-world robotics challenges. Research-oriented mindset and ability to translate experimentation into production-ready autonomy improvements.

Ability to work effectively in cross-functional teams (robotics, embedded, platform/software, operations). Clear communication skills and ability to document technical work, trade-offs, and validation outcomes. High ownership and accountability for results, timelines, and engineering quality.

Adaptability to fast-paced R&D environments involving prototyping, testing, and iterative development. Strong business context understanding, able to translate operational needs into technical solutions. Open to feedback and proactive in applying improvements suggested by senior engineers or tech leads.

Core Backend & Python Strong foundation in Python, including OOP principles, design patterns, and writing clean, maintainable code. Experience building backend services using frameworks such as FastAPI, Flask, or Django. Ability to design, develop, and maintain RESTful APIs with proper error handling and logging.

AWS & Cloud Experience using AWS services, such as Lambda, SQS/SNS, API Gateway, Step Functions, DynamoDB, RDS, S3, and CloudWatch. Experience designing event-driven and serverless architectures. Familiarity with IAM, environment configuration, and cloud security best practices.

Embedded systems focus Strong proficiency in Python and experience using deep learning frameworks Hands-on experience in one or more of the following areas: Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) Optimisation and decision systems for routing, scheduling, dispatch Time-series forecasting and spatiotemporal modelling Graph Ne